Domain Enhanced Analysis of Microarray Data Using GO Annotations

Abstract

New biological systems technologies give scientists the ability to measure thousands of bio-molecules including genes, proteins, lipids and metabolites. We use domain knowledge, e.g., the Gene Ontology, to guide analysis of such data. By focusing on domain-aggregated results at, say the molecular function level, increased interpretability is available to biological scientists beyond what is possible if results are presented at the gene level. We use a 'top-down' approach to perform domain aggregation by first combining gene expressions before testing for differentially expressed patterns. This is in contrast to the more standard 'bottom-up' approach where genes are first tested individually then aggregated by domain knowledge. The benefits are greater sensitivity for detecting signals. In DEA procedure, the first scores from the PLS procedure are used to test for differentially expressed patterns using the t test. We find the general t test inadequate for adjusting for the number of genes within each GO term. New tests are proposed by finding the true null distribution of each PLS score adjusted for the size of the GO term. Our method is assessed using a series of simulation studies. Furthermore, we also discuss the impact of our testing procedure with different coding of our classification response variable, namely 0⁄1 or -1⁄1 for data with two classes.